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A Bayesian Network Model for Reducing Accident Rates of Electrical and Mechanical (E&M) Work
Albert P C Chan1, Francis K W Wong2, Carol K H Hon3
1Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China. albert.chan@polyu.edu.hk.
International Journal of Environmental Research and Public Health
|November 11, 2018
Summary
Improving safety in electrical and mechanical (E&M) repair, maintenance, alteration, and addition (RMAA) work is crucial. A Bayesian Network model identified safety attitude and procedures as key factors in reducing E&M accidents.
Area of Science:
- Occupational Safety and Health
- Risk Management
- Engineering Safety
Background:
- Accidents in Repair, Maintenance, Alteration, and Addition (RMAA) work, particularly in electrical and mechanical (E&M) installations, are a growing concern.
- E&M work in RMAA involves diverse trades, numerous practitioners, and high-risk activities, necessitating specific safety research.
- Understanding causal relationships between safety factors and accident rates is vital for effective safety management in E&M RMAA.
Purpose of the Study:
- To develop a probabilistic model identifying key factors influencing accident numbers in E&M RMAA work.
- To establish a causal network between safety climate, personal experience, and accident occurrences.
- To inform targeted safety management strategies for reducing E&M accidents.
Main Methods:
- Utilized a Bayesian Network (BN) model to map probabilistic relationships between causal factors and accident numbers.
- Collected data through a survey questionnaire administered to 155 E&M practitioners.
- Analyzed safety climate and personal experience factors impacting accident rates.
Main Results:
- The Bayesian Network analysis identified safety attitude and safety procedures as the most significant factors for accident reduction.
- Demonstrated the probabilistic influence of various safety factors on the frequency of E&M accidents.
- Highlighted the effectiveness of controlling single or multiple factors to mitigate accident risks.
Conclusions:
- Safety attitude and adherence to safety procedures are paramount in minimizing accidents in E&M RMAA.
- The proposed BN model offers a tool for optimizing resource allocation in safety management.
- Implementing targeted interventions based on BN insights can significantly improve safety performance in E&M RMAA projects.
Keywords:
Bayesian NetworksM) worksaccident analysiselectrical and mechanical (E&safety managementMore Related Videos
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